Recently, there has been some effort to devise explainable methods for unsupervised learning tasks, in particular, for clustering [1]}, [2]}. We investigate the framework discussed by [1]}, where an explainable clustering
is given by a partition, induced by the leaves of an axis-aligned decision tree, that optimizes
some predefined objective function.
